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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and Applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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Fine-grained distributed averaging for large-scale radio interferometric measurement sets

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Research in Astronomy and Astrophysics 2021年第4期21卷 93-100页

作者： Shou-Lin Wei Kai-Da Luo Feng Wang Hui Deng Ying Mei Faculty of Information Engineering and Automation Computer Technology Application Key Lab of Yunnan ProvinceKunming University of Science and TechnologyKunming 650500China Center for Astrophysics Guangzhou UniversityGuangzhou 510006China

The Square Kilometre Array(SKA)would be the world’s largest radio telescope with eventually over a square kilometre of collecting ***,there are enormous challenges in its data *** use of modern distributed computing techniques to solve the problem of massive data processing in the SKA is one of the most important *** this study,basing on the Dask distribution computational framework,and taking the visibility function integral processing as an example,we adopt a multi-level parallelism method to implement distributed averaging over time and *** Array was used to implement super large matrix or arrays with supported *** maximize the usage of memory,we further exploit the data parallelism provided by Dask that intelligently distributes the computational load across a network of computer agents and has a built-in fault tolerance *** validity of the proposed pattern was also verified by using the Common Astronomy Software Application(CASA),wherein we analyze the smearing effects on images reconstructed from different resolution visibilities.

关键词： techniques:interferometric methods:data analysis methods:numerical instrumentation:interferometers

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Predictive maintenance for electrical motors: Current approach and usage of artificial intelligence algorithms

Predictive maintenance for electrical motors: Current approa...

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International Conference on Methods and Models in automation and Robotics (MMAR)

作者： Tomasz Herzog Krzysztof Bartecki Faculty of Electrical Engineering Automatic Control and Computer Science Opole University of Technology Opole Poland Rockwell Automation Power Control Business Katowice Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The article presents the current approach in artificial intelligence methods used in predictive maintenance for electrical motors. The study focuses on presenting data classification methods for fault detection and isolation using data from condition monitoring systems. We discuss the advantages and disadvantages of using machine learning methods like perceptron and Support Vector Machine (SVM) for fault classification problems. Our conclusion shows great interest in machine learning methods combined with condition monitoring systems. Our future research will focus on comparing SVM, perceptron networks, and other methods, such as K-nearest neighbors, with their possibility for implementation in embedded firmware systems.

关键词： Support vector machines Condition monitoring Accuracy Fault detection Artificial neural networks Machine learning Prediction algorithms

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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A multi-view comparative learning method for spatial transcriptomics data clustering

A multi-view comparative learning method for spatial transcr...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Peng, Wei Zhang, Zhihao Dai, Wei Fu, Xiaodong Liu, Li Liu, Lijun Yu, Ning Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming China Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China State University of New York The College at Brockport Department of Computing Sciences BrockportNY14422 United States

ISBN: (纸本)9798350337488

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions. © 2023 IEEE.

关键词： contrast learning multiview learning spatial transcriptome data clustering

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Spformer: Shape-Aware Medical Image Segmentation Via Partitioning Frequency Bands

SSRN

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SSRN 2024年

作者： Zhou, Ke Chen, Tianxiang Huang, Jiayuan Fu, Dongmei Qi, Chuanjiang School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China University of Science and Technology of China Hefei China Faculty of Computer Science Universiti Putra Malaysia Serdang Malaysia

Precise medical image segmentation is crucial for computer-aided diagnosis, but current methods struggle with subtle areas in endoscopic views where lesions and normal tissue look similar. We propose SPFormer (Shape-aware Partitioning Former), a dual-branch image segmentation model to address this issue. The high-frequency branch captures edges and fine details, while the low-frequency branch focuses on overall shape and color distribution. SPFormer includes a hybrid decoder and a chimeric wavelet block, merging dual-branch information through continuous bilateral interaction. We also designed a dual-domain loss function to comprehensively evaluate the differences between the model output and the ground truth, especially when pixel value differences are small but frequency domain differences are *** enhances the accuracy and efficiency of computer-aided diagnosis, particularly in polyp and skin disease segmentation. Accurately understanding the shape and volume of lesions is crucial for disease grading and treatment planning. SPFormer outperforms existing CNN-Transformer (fusion of Convolutional Neural Network and Transformer) models, providing a more robust and reliable tool for medical image analysis. On the Kvasir-SEG (an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks), SPFormer achieves a 15.29% higher IoU (Intersection over Union) than TransUNet (Transformer-based U-shaped Network), and a reduction of 33.75 GFLOPs (Giga Floating Point Operations per Second). Compared to H2Former (Hierarchical Hybrid Transformer), SPFormer demonstrates an improvement of 5.93% in IoU, and a reduction of 11.74 GFLOPs. © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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R2F-UGCGAN: a regional fusion factor-based union gradient and contrast generative adversarial network for infrared and visible image fusion

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Optica Acta: International Journal of Optics 2023年第1期70卷 52-68页

作者： Yuning Xie Gang Liu Rui Xu Durga Prasad Bavirisetti Haojie Tang Mengliang Xing a School of Automation Engineering Shanghai University of Electric Power Shanghai People's Republic of China b Faculty of Information Technology and Electrical Engineering Department of Computer Science Norwegian University of Science and Technology Trondheim Norway

ABSTRACTABSTRACTTo efficiently preserve texture and target information in source images, an image fusion algorithm of Regional Fusion Factor-Based Union Gradient and Contrast Generative Adversarial Network (R2F-UGCGAN) is proposed. Firstly, an adaptive gradient diffusion (AGD) decomposition algorithm is designed to extract representative features. A pair of infrared (IR) and visible (VIS) images are decomposed by AGD to obtain low-frequency components with salient targets and high-frequency components with rich edge gradient information. Secondly, In the high-frequency components, principal component analysis (PCA) is used for fusion to obtain more detailed images with texture gradients. R2F-UGCGAN is used to fuse the low-frequency components, which can effectively ensure good consistency between the target region and the background region. Therefore, a fused image is produced, which inherits more thermal radiation information and important texture details. Finally, subjective and objective comparison experiments are performed on TNO and RoadScene datasets with state-of-the-art image fusion methods. The experimental results of R2F-UGCGAN are prominent and consistent compared to these fusion algorithms in terms of both subjective and objective evaluation.

关键词： Image fusion infrared visible generative adversarial network PCA adaptive gradient diffusion

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Efficient One-Stage Deep Learning for Text Detection in Scene Images

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 65-71页

作者： Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Faculty of Exact Sciences Natural And Life Sciences University Mohamed Khider of Biskra Algeria Computer Science Department University Mohamed Khider of Biskra Algeria Department of Computer Automation and Management Engineering Sapienza University of Rome Italy

Text detection in natural images is a crucial task for extracting and recognizing valuable information, but it comes with significant challenges. Traditional image processing methods often rely on synthetic features, which may not be suitable for handling the diverse range of unstructured text scenarios encountered in real-world environments. To overcome this challenge, deep learning techniques have emerged as a powerful tool for adaptively learning and extracting features to digitize text. In this paper, we introduce a novel approach based on the YOLOv5 architecture for text detection. Our method leverages the strengths of YOLOv5, including its efficient and lightweight architecture, multi-scale prediction capability, and advanced data augmentation techniques. By employing these features, our proposed model can accurately identify regions in an image that are more likely to contain text. Through extensive experimentation on popular benchmark datasets, we have evaluated the performance of our proposed model. The results have demonstrated that our method outperforms state-of-the-art approaches in terms of both detection efficiency and accuracy. This indicates the effectiveness of our approach in handling the challenges posed by text detection in natural images. © Editura ELECTRA 2024. All rights reserved.

关键词： Deep Learning Natural scene image Text detection Yolov5

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A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

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Digital Communications and Networks 2022年第6期8卷 911-922页

作者： Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang College of Computer Science Chongqing UniversityChongqing400030China School of Computer and Information Engineering Jiangxi Normal UniversityJiangxi330022China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu611731China Mashang Consumer Finance Co. Ltd.(MSCF)Chongqing401129China Global Energy Interconnection Research Institute Co.Ltd. Beijing102209China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan650500China CISDI Research&Development CO. LTDChongqing401122China

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

关键词： Edge computing Fault tolerance DQN algorithm Primary-backup model

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Bidirectional Patch-based Correlations with Local Rigidity for Global Nonrigid Registration

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IEEE Transactions on Circuits and Systems for Video Technology 2024年 1-1页

作者： Zhao, Mingyuan Yu, Xuexin Hu, Xinggang Wang, Fei Xu, Long Ji, Xiangyang National Astronomical Observatories Chinese Academy of Sciences Beijing China Department of Automation Tsinghua University Beijing China School of Computer Science and Technology Dalian University of Technology Dalian China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

The registration of time-varying 3D shapes with high degrees of freedom remains a challenging task. Most existing techniques attempt to address this issue by solving an optimization problem defined on deformation graph with as-rigid-as-possible smoothness prior, which usually struggle to capture large scale displacements. Motivated by the insight that a set of points tends to collectively undergo significant rigid motion accompanied by slight nonrigid deformation, we propose a two-step approach to address nonrigid registration in a coarse-to- fine manner. In the first step, coarse correlations between source and target points are constructed by estimating a set of rigid transformations for local patches which are regional clusters of points. To leverage more contextual information, a bidirectional registration module is introduced that estimates both the forward and backward patch-wise rigid transformation fields (PRTFs). Subsequently, in the second step, the source point set is warped by blending both forward and backward PRTFs and fed into a deformation optimization module. Here, unidirectional point-based correspondences are sought to refine the global nonrigid transformation fields (GNTFs) while adhering to local rigidity constraints. To illustrate the efficacy of our method, we conduct tests on challenging scenarios involving human datasets, including large displacements resulting from fast inter-frame motions or pose changes. Both qualitative and quantitative results demonstrate that our approach outperforms several state-of-the-art methods in terms of robustness and registration accuracy. IEEE

关键词： Three dimensional displays

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